Table REF  shows WER on the test dataset of each model. The proposed method achieved the best WER performance and reduced WER by 12% compared to the plain ASR backend, Conformer(L). SE+Conformer(L) also outperformed the plain ASR backends. From these results, the use of multi condition/task learning [1]}, [2]}, backend pre-training using clean data, and SE frontends is effective than merely increasing the ASR backend model size.
